import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
				from scipy.stats import pearsonr, spearmanr
				from sklearn.metrics import fa_score, matthews_corrcoef


A						=			(
    'This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '
    'library. You can have a look at this example script for pointers: '
    'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'
)


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[int]			,       UpperCamelCase     :				Optional[int]							)   ->    str:
		warnings.warn(UpperCamelCase			,       UpperCamelCase							)
		requires_backends(UpperCamelCase			,       'sklearn'							)
		return (preds == labels).mean()


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple			,       UpperCamelCase     :				List[str]							)   ->    Optional[int]:
		warnings.warn(UpperCamelCase			,       UpperCamelCase							)
		requires_backends(UpperCamelCase			,       'sklearn'							)
		_lowerCamelCase				   =  simple_accuracy(UpperCamelCase			,       UpperCamelCase							)
		_lowerCamelCase				   =  fa_score(y_true=UpperCamelCase			,       y_pred=UpperCamelCase							)
		return {
		    "acc": acc,
		    "f1": fa,
		    "acc_and_f1": (acc + fa) / 2,
		}


def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]			,       UpperCamelCase     :				Optional[int]							)   ->    Any:
		warnings.warn(UpperCamelCase			,       UpperCamelCase							)
		requires_backends(UpperCamelCase			,       'sklearn'							)
		_lowerCamelCase				   =  pearsonr(UpperCamelCase			,       UpperCamelCase							)[0]
		_lowerCamelCase				   =  spearmanr(UpperCamelCase			,       UpperCamelCase							)[0]
		return {
		    "pearson": pearson_corr,
		    "spearmanr": spearman_corr,
		    "corr": (pearson_corr + spearman_corr) / 2,
		}


def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]			,       UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				Any							)   ->    str:
		warnings.warn(UpperCamelCase			,       UpperCamelCase							)
		requires_backends(UpperCamelCase			,       'sklearn'							)
		assert len(UpperCamelCase							) == len(UpperCamelCase							), F"""Predictions and labels have mismatched lengths {len(UpperCamelCase							)} and {len(UpperCamelCase							)}"""
		if task_name == "cola":
				return {"mcc": matthews_corrcoef(UpperCamelCase			,       UpperCamelCase							)}
		elif task_name == "sst-2":
				return {"acc": simple_accuracy(UpperCamelCase			,       UpperCamelCase							)}
		elif task_name == "mrpc":
				return acc_and_fa(UpperCamelCase			,       UpperCamelCase							)
		elif task_name == "sts-b":
				return pearson_and_spearman(UpperCamelCase			,       UpperCamelCase							)
		elif task_name == "qqp":
				return acc_and_fa(UpperCamelCase			,       UpperCamelCase							)
		elif task_name == "mnli":
				return {"mnli/acc": simple_accuracy(UpperCamelCase			,       UpperCamelCase							)}
		elif task_name == "mnli-mm":
				return {"mnli-mm/acc": simple_accuracy(UpperCamelCase			,       UpperCamelCase							)}
		elif task_name == "qnli":
				return {"acc": simple_accuracy(UpperCamelCase			,       UpperCamelCase							)}
		elif task_name == "rte":
				return {"acc": simple_accuracy(UpperCamelCase			,       UpperCamelCase							)}
		elif task_name == "wnli":
				return {"acc": simple_accuracy(UpperCamelCase			,       UpperCamelCase							)}
		elif task_name == "hans":
				return {"acc": simple_accuracy(UpperCamelCase			,       UpperCamelCase							)}
		else:
				raise KeyError(UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				Optional[Any]			,       UpperCamelCase     :				str							)   ->    List[Any]:
		warnings.warn(UpperCamelCase			,       UpperCamelCase							)
		requires_backends(UpperCamelCase			,       'sklearn'							)
		if len(UpperCamelCase							) != len(UpperCamelCase							):
				raise ValueError(F"""Predictions and labels have mismatched lengths {len(UpperCamelCase							)} and {len(UpperCamelCase							)}"""							)
		if task_name == "xnli":
				return {"acc": simple_accuracy(UpperCamelCase			,       UpperCamelCase							)}
		else:
				raise KeyError(UpperCamelCase							)
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)


def 	lowerCamelCase					(	UpperCamelCase     :				int							)   ->    Union[str, Any]:
		_lowerCamelCase				   =  torch.exp(UpperCamelCase							)
		_lowerCamelCase				   =  torch.sum(UpperCamelCase			,       dim=1							)  # sum of exp(x_i)
		_lowerCamelCase				   =  torch.sum(x * exp_x			,       dim=1							)  # sum of x_i * exp(x_i)
		return torch.log(UpperCamelCase							) - B / A





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Union[str, Any]     ,  snake_case__     :     Any		)      ->	Optional[Any]:
				super().__init__()
				_lowerCamelCase				   =  config.output_attentions
				_lowerCamelCase				   =  config.output_hidden_states
				_lowerCamelCase				   =  nn.ModuleList([BertLayer(snake_case__		) for _ in range(config.num_hidden_layers		)]		)
				_lowerCamelCase				   =  nn.ModuleList([BertHighway(snake_case__		) for _ in range(config.num_hidden_layers		)]		)

				_lowerCamelCase				   =  [-1 for _ in range(config.num_hidden_layers		)]
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     str		)      ->	Optional[int]:
				if (type(snake_case__		) is float) or (type(snake_case__		) is int):
						for i in range(len(self.early_exit_entropy		)		):
								_lowerCamelCase				   =  x
				else:
						_lowerCamelCase				   =  x
		def   _snake_case       (       self     :     Tuple     ,  snake_case__     :     Optional[int]		)      ->	int:
				_lowerCamelCase				   =  pooler.state_dict()
				for highway in self.highway:
						for name, param in highway.pooler.state_dict().items():
								param.copy_(loaded_model[name]		)



		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Union[str, Any]     ,  snake_case__     :     int=None     ,  snake_case__     :     Optional[int]=None     ,  snake_case__     :     List[str]=None     ,  snake_case__     :     Dict=None     ,  )      ->	List[str]:
				_lowerCamelCase				   =  ()
				_lowerCamelCase				   =  ()
				_lowerCamelCase				   =  ()
				for i, layer_module in enumerate(self.layer		):
						if self.output_hidden_states:
								_lowerCamelCase				   =  all_hidden_states + (hidden_states,)

						_lowerCamelCase				   =  layer_module(
						    snake_case__     ,  snake_case__     ,  head_mask[i]     ,  snake_case__     ,  snake_case__		)
						_lowerCamelCase				   =  layer_outputs[0]

						if self.output_attentions:
								_lowerCamelCase				   =  all_attentions + (layer_outputs[1],)

						_lowerCamelCase				   =  (hidden_states,)
						if self.output_hidden_states:
								_lowerCamelCase				   =  current_outputs + (all_hidden_states,)
						if self.output_attentions:
								_lowerCamelCase				   =  current_outputs + (all_attentions,)

						_lowerCamelCase				   =  self.highway[i](snake_case__		)
						# logits, pooled_output

						if not self.training:
								_lowerCamelCase				   =  highway_exit[0]
								_lowerCamelCase				   =  entropy(snake_case__		)
								_lowerCamelCase				   =  highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
								_lowerCamelCase				   =  all_highway_exits + (highway_exit,)

								if highway_entropy < self.early_exit_entropy[i]:
										_lowerCamelCase				   =  (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
										raise HighwayException(snake_case__     ,  i + 1		)
						else:
								_lowerCamelCase				   =  all_highway_exits + (highway_exit,)

        # Add last layer
				if self.output_hidden_states:
						_lowerCamelCase				   =  all_hidden_states + (hidden_states,)

				_lowerCamelCase				   =  (hidden_states,)
				if self.output_hidden_states:
						_lowerCamelCase				   =  outputs + (all_hidden_states,)
				if self.output_attentions:
						_lowerCamelCase				   =  outputs + (all_attentions,)

				_lowerCamelCase				   =  outputs + (all_highway_exits,)
				return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits





@add_start_docstrings(
    'The Bert Model transformer with early exiting (DeeBERT). '     ,SCREAMING_SNAKE_CASE_     ,)
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __init__(       self     :     Dict     ,  snake_case__     :     List[Any]		)      ->	Any:
				super().__init__(snake_case__		)
				_lowerCamelCase				   =  config

				_lowerCamelCase				   =  BertEmbeddings(snake_case__		)
				_lowerCamelCase				   =  DeeBertEncoder(snake_case__		)
				_lowerCamelCase				   =  BertPooler(snake_case__		)

				self.init_weights()
		def   _snake_case       (       self     :     Tuple		)      ->	Tuple:
				self.encoder.init_highway_pooler(self.pooler		)
		def   _snake_case       (       self     :     int		)      ->	List[Any]:
				return self.embeddings.word_embeddings
		def   _snake_case       (       self     :     Optional[int]     ,  snake_case__     :     List[Any]		)      ->	Any:
				_lowerCamelCase				   =  value
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     int		)      ->	Any:
				for layer, heads in heads_to_prune.items():
						self.encoder.layer[layer].attention.prune_heads(snake_case__		)



		@add_start_docstrings_to_model_forward(snake_case__		)
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Dict=None     ,  snake_case__     :     Any=None     ,  snake_case__     :     Dict=None     ,  snake_case__     :     Any=None     ,  snake_case__     :     List[str]=None     ,  snake_case__     :     Union[str, Any]=None     ,  snake_case__     :     str=None     ,  snake_case__     :     List[Any]=None     ,  )      ->	List[str]:
				if input_ids is not None and inputs_embeds is not None:
						raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time'		)
				elif input_ids is not None:
						_lowerCamelCase				   =  input_ids.size()
				elif inputs_embeds is not None:
						_lowerCamelCase				   =  inputs_embeds.size()[:-1]
				else:
						raise ValueError('You have to specify either input_ids or inputs_embeds'		)

				_lowerCamelCase				   =  input_ids.device if input_ids is not None else inputs_embeds.device

				if attention_mask is None:
						_lowerCamelCase				   =  torch.ones(snake_case__     ,  device=snake_case__		)
				if encoder_attention_mask is None:
						_lowerCamelCase				   =  torch.ones(snake_case__     ,  device=snake_case__		)
				if token_type_ids is None:
						_lowerCamelCase				   =  torch.zeros(snake_case__     ,  dtype=torch.long     ,  device=snake_case__		)

				# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
				# ourselves in which case we just need to make it broadcastable to all heads.
				_lowerCamelCase				   =  self.get_extended_attention_mask(snake_case__     ,  snake_case__     ,  snake_case__		)

				# If a 2D ou 3D attention mask is provided for the cross-attention
				# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
				if encoder_attention_mask.dim() == 3:
						_lowerCamelCase				   =  encoder_attention_mask[:, None, :, :]
				if encoder_attention_mask.dim() == 2:
						_lowerCamelCase				   =  encoder_attention_mask[:, None, None, :]

				_lowerCamelCase				   =  encoder_extended_attention_mask.to(
				    dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
				_lowerCamelCase				   =  (1.0 - encoder_extended_attention_mask) * -1_0000.0

				# Prepare head mask if needed
				# 1.0 in head_mask indicate we keep the head
				# attention_probs has shape bsz x n_heads x N x N
				# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
				# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
				_lowerCamelCase				   =  self.get_head_mask(snake_case__     ,  self.config.num_hidden_layers		)

				_lowerCamelCase				   =  self.embeddings(
				    input_ids=snake_case__     ,  position_ids=snake_case__     ,  token_type_ids=snake_case__     ,  inputs_embeds=snake_case__		)
				_lowerCamelCase				   =  self.encoder(
				    snake_case__     ,  attention_mask=snake_case__     ,  head_mask=snake_case__     ,  encoder_hidden_states=snake_case__     ,  encoder_attention_mask=snake_case__     ,  )
				_lowerCamelCase				   =  encoder_outputs[0]
				_lowerCamelCase				   =  self.pooler(snake_case__		)

				_lowerCamelCase				   =  (
				    sequence_output,
				    pooled_output,
				) + encoder_outputs[
				    1:
				]  # add hidden_states and attentions if they are here
				return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __init__(       self     :     Optional[int]     ,  snake_case__     :     Dict     ,  snake_case__     :     Any		)      ->	str:
				_lowerCamelCase				   =  message
				_lowerCamelCase				   =  exit_layer  # start from 1!





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Union[str, Any]     ,  snake_case__     :     Optional[Any]		)      ->	Any:
				super().__init__()
				_lowerCamelCase				   =  BertPooler(snake_case__		)
				_lowerCamelCase				   =  nn.Dropout(config.hidden_dropout_prob		)
				_lowerCamelCase				   =  nn.Linear(config.hidden_size     ,  config.num_labels		)



		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     Dict		)      ->	Optional[Any]:
				# Pooler
				_lowerCamelCase				   =  encoder_outputs[0]
				_lowerCamelCase				   =  self.pooler(snake_case__		)
				# "return" pooler_output

				# BertModel
				_lowerCamelCase				   =  (pooler_input, pooler_output) + encoder_outputs[1:]
				# "return" bmodel_output

				# Dropout and classification
				_lowerCamelCase				   =  bmodel_output[1]

				_lowerCamelCase				   =  self.dropout(snake_case__		)
				_lowerCamelCase				   =  self.classifier(snake_case__		)

				return logits, pooled_output





@add_start_docstrings(
    'Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. '     ,SCREAMING_SNAKE_CASE_     ,)
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __init__(       self     :     Optional[int]     ,  snake_case__     :     Dict		)      ->	Dict:
				super().__init__(snake_case__		)
				_lowerCamelCase				   =  config.num_labels
				_lowerCamelCase				   =  config.num_hidden_layers

				_lowerCamelCase				   =  DeeBertModel(snake_case__		)
				_lowerCamelCase				   =  nn.Dropout(config.hidden_dropout_prob		)
				_lowerCamelCase				   =  nn.Linear(config.hidden_size     ,  self.config.num_labels		)

				self.init_weights()



		@add_start_docstrings_to_model_forward(snake_case__		)
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Union[str, Any]=None     ,  snake_case__     :     Optional[int]=None     ,  snake_case__     :     str=None     ,  snake_case__     :     Dict=None     ,  snake_case__     :     str=None     ,  snake_case__     :     Union[str, Any]=None     ,  snake_case__     :     int=None     ,  snake_case__     :     Union[str, Any]=-1     ,  snake_case__     :     Any=False     ,  )      ->	Union[str, Any]:
				_lowerCamelCase				   =  self.num_layers
				try:
						_lowerCamelCase				   =  self.bert(
						    snake_case__     ,  attention_mask=snake_case__     ,  token_type_ids=snake_case__     ,  position_ids=snake_case__     ,  head_mask=snake_case__     ,  inputs_embeds=snake_case__     ,  )
						# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

						_lowerCamelCase				   =  outputs[1]

						_lowerCamelCase				   =  self.dropout(snake_case__		)
						_lowerCamelCase				   =  self.classifier(snake_case__		)
						_lowerCamelCase				   =  (logits,) + outputs[2:]  # add hidden states and attention if they are here
				except HighwayException as e:
						_lowerCamelCase				   =  e.message
						_lowerCamelCase				   =  e.exit_layer
						_lowerCamelCase				   =  outputs[0]

				if not self.training:
						_lowerCamelCase				   =  entropy(snake_case__		)
						_lowerCamelCase				   =  []
						_lowerCamelCase				   =  []
				if labels is not None:
						if self.num_labels == 1:
								#  We are doing regression
								_lowerCamelCase				   =  MSELoss()
								_lowerCamelCase				   =  loss_fct(logits.view(-1		)     ,  labels.view(-1		)		)
						else:
								_lowerCamelCase				   =  CrossEntropyLoss()
								_lowerCamelCase				   =  loss_fct(logits.view(-1     ,  self.num_labels		)     ,  labels.view(-1		)		)

						# work with highway exits
						_lowerCamelCase				   =  []
						for highway_exit in outputs[-1]:
								_lowerCamelCase				   =  highway_exit[0]
								if not self.training:
										highway_logits_all.append(snake_case__		)
										highway_entropy.append(highway_exit[2]		)
								if self.num_labels == 1:
										#  We are doing regression
										_lowerCamelCase				   =  MSELoss()
										_lowerCamelCase				   =  loss_fct(highway_logits.view(-1		)     ,  labels.view(-1		)		)
								else:
										_lowerCamelCase				   =  CrossEntropyLoss()
										_lowerCamelCase				   =  loss_fct(highway_logits.view(-1     ,  self.num_labels		)     ,  labels.view(-1		)		)
								highway_losses.append(snake_case__		)

						if train_highway:
								_lowerCamelCase				   =  (sum(highway_losses[:-1]		),) + outputs
								# exclude the final highway, of course
						else:
								_lowerCamelCase				   =  (loss,) + outputs
				if not self.training:
						_lowerCamelCase				   =  outputs + ((original_entropy, highway_entropy), exit_layer)
						if output_layer >= 0:
								_lowerCamelCase				   =  (
								    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
								)  # use the highway of the last layer

				return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)